The sensitivities revealed by a sensitivity analysis of a probabilistic network typically depend on the entered evidence. For a real-life network therefore, the analysis is perfor...
Sensitivity analysis of Markovian models amounts to computing the constants in polynomial functions of a parameter under study. To handle the computational complexity involved, we...
While known algorithms for sensitivity analysis and parameter tuning in probabilistic networks have a running time that is exponential in the size of the network, the exact comput...
Sensitivity analysis is a method for extracting the cause and effect relationship between the inputs and outputs of the network. After training a neural network, one may want to k...
Software engineering is plagued by problems associated with unreliable cost estimates. This paper introduces an approach to sensitivity analysis for requirements engineering. It u...
The reliability of ADCs used in highly critical systems can be increased by applying a two-step procedure starting with sensitivity analysis followed by redesign. The sensitivity ...
The European call option prices have well-known formulae in the Cox-RossRubinstein model [2], depending on the volatility of the underlying asset. Nevertheless it is hard to give ...
The parameters in these software reliability models are usually directly obtained from the field failure data. Due to the dynamic properties of the system and the insufficiency of...
Jung-Hua Lo, Chin-Yu Huang, Sy-Yen Kuo, Michael R....
A sensitivity analysis of a single-server, infinite-buffer queue with correlated arrivals and correlated service times is performed. We study and compare the isolated impact of (...
During real-world design of embedded real-time systems, it cannot be expected that all performance data required for scheduling analysis is fully available up front. In such situa...